Agricultural modeling system with data fusion and related server computing resource and methods

ABSTRACT

An agricultural modeling system may include a mobile LiDAR platform configured to generate 3D point cloud data of an agricultural geographic area, client devices, a geospatial database configured to store a data layer for the agricultural geographic area, and a server computing resource in communication with the mobile LiDAR platform, the client devices, and the geospatial database. The server computing resource may be configured to geographically reference the data layer fused with the 3D point cloud data of the agricultural geographic area, and generate a multi-layered data model for the geographically referenced data layer fused with the 3D point cloud data of the agricultural geographic area for providing interactive analysis and visualization of agricultural information alongside collaborators with LiDAR-based high resolution 3D plant model. A client device may be configured to selectively render the multi-layered data model.

TECHNICAL FIELD

The present disclosure relates to the field of geospatial systems, and,more particularly, to an agricultural modeling system and relatedmethods.

BACKGROUND

Agriculture is an endeavor practiced for thousands of years.Nevertheless, the industry of agriculture has advanced greatly in thelast few decades. Key tasks within agriculture are forecasting andprediction of crop yields, reduction of costs of production, andensuring sustainable production practices. A great deal of productiondata is collected throughout the season, however, this data is collectedat different scales, on different systems, and with differentmethodologies. Current approaches consider each data sourceindependently and may not aggregate data. Inaccuracies lead to increasedproduction costs, poor forecasting and yield estimates, and greaterenvironmental impact. In order to enhance the value of these differentdata sources, it may be helpful to organize and link across all of thesedifferent scales, systems, and methodologies. This may requiredigitizing all of these data sources, including a high-resolution baselayer including a per-plant three-dimensional (3D) model. This may allowfor the linkages of all of these various data sources.

One advantageous approach is disclosed in U.S. Pat. No. 10,534,086 toMcPeek, assigned to the present application's assignee, the contents arewhich are hereby incorporated by reference in their entirety. Thissystem is for improved real-time yield monitoring and sensor fusion ofcrops in an orchard. The system may include a collection vehicle, amodular processing unit, a volume measurement module, a 3D point cloudscanning module, an inertial navigation system, and a post-processingserver.

SUMMARY

Generally, an agricultural modeling system is used for processing datafor an agricultural geographic area. The agricultural modeling systemmay include a mobile light detection and ranging (LiDAR) platformconfigured to generate 3D point cloud data of the agriculturalgeographic area, a plurality of client devices, a geospatial databaseconfigured to store at least one data layer for the agriculturalgeographic area, and a server computing resource (e.g. cloud computingresources, a standalone computing device, a computing cluster, or acombination thereof) in communication with the mobile LiDAR platform,the plurality of client devices, and the geospatial database. The servercomputing resource/cluster may be configured to geographically referencethe at least one data layer fused with the 3D point cloud data of theagricultural geographic area, and generate a multi-layered data modelfor the geographically referenced at least one data layer fused with the3D point cloud data of the agricultural geographic area for providinginteractive analysis and visualization of agricultural informationalongside collaborators with LiDAR-based high resolution 3D plant model.A given client device from the plurality of client devices may beconfigured to selectively render the multi-layered data model.

Additionally, the at least one data layer for the agriculturalgeographic area may comprise a plurality of data layers for theagricultural geographic area comprising a climate data source, a soilsurvey data source, a satellite imagery data source, and a treephenotyping data source, for example. The at least one data layer forthe agricultural geographic area may have a resolution less than orequal to the 3D point cloud data of the agricultural geographic area.The 3D point cloud data may comprise a Keyhole Markup Language polygondefining the agricultural geographic area.

In some embodiments, the server computing resource and the given clientdevice may be configured to execute a programming code script, and thegiven client device may be configured to selectively render themulti-layered data model based upon the programming code script. Thegiven client device may be configured to upload an additional datasource to the server computing resource, and the server computingresource may be configured to geographically reference the additionaldata source and the 3D point cloud data of the agricultural geographicarea.

Also, the server computing resource may be configured to generate adifferent multi-layered data model for each client device using arespective different at least one data layer. The given client devicemay comprise a notebook client device, and the at least one data layermay be associated with at least one notebook workflow. The mobile LiDARplatform may comprise at least one of an airborne platform and a groundplatform, for instance.

Another aspect is directed to a server computing resource in anagricultural modeling system for processing data for an agriculturalgeographic area. The agricultural modeling system may include a mobileLiDAR platform configured to generate 3D point cloud data of theagricultural geographic area, a plurality of client devices, and ageospatial database configured to store at least one data layer for theagricultural geographic area. The server computing resource may includea processing unit and storage device or a group of processing nodes anda distributed storage service cooperating therewith and configured toreceive the 3D point cloud data of the agricultural geographic area fromthe mobile LiDAR platform, and receive the at least one data layer forthe agricultural geographic area from the geospatial database. Theprocessing unit may be configured to geographically reference the atleast one data layer fused with the 3D point cloud data of theagricultural geographic area, and generate a multi-layered data modelfor the geographically referenced at least one data layer fused with the3D point cloud data of the agricultural geographic area for providinginteractive analysis and visualization of agricultural informationalongside collaborators with LiDAR-based high resolution 3D plant model.A given client device from the plurality of client devices may beconfigured to selectively render the multi-layered data model.

Yet another aspect is directed to a method for operating a servercomputing resource in an agricultural modeling system for processingdata for an agricultural geographic area. The agricultural modelingsystem may include a mobile LiDAR platform configured to generate 3Dpoint cloud data of the agricultural geographic area, a plurality ofclient devices, and a geospatial database configured to store at leastone data layer for the agricultural geographic area. The method mayinclude receiving the 3D point cloud data of the agricultural geographicarea from the mobile LiDAR platform, and receiving the at least one datalayer for the agricultural geographic area from the geospatial database,and geographically referencing the at least one data layer fused withthe 3D point cloud data of the agricultural geographic area. The methodmay comprise generating a multi-layered data model for thegeographically referenced at least one data layer fused with the 3Dpoint cloud data of the agricultural geographic area for providinginteractive analysis and visualization of agricultural informationalongside collaborators with LiDAR-based high resolution 3D plant model.A given client device from the plurality of client devices may beconfigured to selectively render the multi-layered data model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary environment of a firstembodiment of an agricultural modeling system, according to the presentdisclosure.

FIG. 2 is a flowchart for a method of operating a server computingresource in the agricultural modeling system of FIG. 1.

FIG. 3 is a schematic diagram of layers in the multi-layered data modelin the agricultural modeling system of FIG. 1.

FIGS. 4-5 are schematic diagrams of geographically referencing layers inthe multi-layered data model in the agricultural modeling system of FIG.1.

FIG. 6A is a schematic diagram of an exemplary environment of a secondembodiment of the agricultural modeling system, according to the presentdisclosure.

FIGS. 6B-6D are schematic diagrams of data flows in exemplaryapplications in the agricultural modeling system of FIG. 6A.

FIGS. 7A & 7B, and 8-11 are diagrams of the rendered multi-layered datamodel in the agricultural modeling system of FIG. 6A.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which several embodiments ofthe invention are shown. This present disclosure may, however, beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. Rather, these embodiments areprovided so that this disclosure will be thorough and complete, and willfully convey the scope of the present disclosure to those skilled in theart. Like numbers refer to like elements throughout, and base 100reference numerals are used to indicate similar elements in alternativeembodiments.

Precision Agriculture (PA) technologies are being adopted throughoutmany different areas of agricultural industries as their high-techinfrastructures and software ecosystems are continuously evolving, suchas LiDAR sensor devices, drones, robots, 5G wireless networks, bigdata/supercomputing clusters, artificial intelligence (AI), cloudcomputing environment, and cloud native geospatial frameworks. Over thepast decade, the scale and complexity of agricultural technologyhardware and software systems have advanced from a single sensor sourcewith only a few gigabytes collection data to bundled sources from asensor group streaming out terabyte- and even petabyte-scale collectiondatasets. In other words, the amount of information technology (IT)resource overhead in agricultural technology is growing.

On the day-to-day operation, consuming large volume PA data sources notonly brings a technical challenge for the traditional ITinfrastructures, but also adds a significant overhead for grower andagronomist to learn and accept the emerging agricultural technologies.All of this complexity and expense have led to the relatively slowadoption of PA-driven farm operation, management, and risk assessmentacross the United States. However, in recent years, growers, especially,research teams in a farm organization have become more and more engagedinto the emerging potentials happening in this particular area as theagricultural science keeps pushing its capacity and capability into thenext level.

Referring initially to FIGS. 1-5, an agricultural modeling system 100according to the present disclosure is now described. The agriculturalmodeling system 100 is for processing data for an agriculturalgeographic area 101 and provides an approach to the above describedproblems with typical technologies. The agricultural modeling system 100is designed to bridge the technology adoption gap between end users andthe LiDAR software stack and help growers or researchers to process,analyze, and share the large-scale ground/aerial LiDAR data sourcesintuitively and transparently.

The agricultural modeling system 100 illustratively includes a mobileLiDAR platform 102 configured to generate 3D point cloud data 103 of theagricultural geographic area 101. The mobile LiDAR platform 102 mayinclude either or both of an airborne platform (e.g. unmanned aerialvehicle (UAV), aircraft) or a ground platform (e.g. tripod or groundvehicle mounted sensor). Also, in some embodiments, the 3D point clouddata 103 may comprise a KML polygon defining the boundaries of theagricultural geographic area 101.

The agricultural modeling system 100 illustratively comprises aplurality of client devices 104 a-104 b. For example, each of theplurality of client devices 104 a-104 b may comprise, but are notlimited to, a desktop computer, a personal laptop, a portable tabletdevice, a smart mobile phone, an embedded computer on the mobile LiDARdevice, and/or an automobile computer system. In some embodiments, eachof the plurality of client devices 104 a-104 b may be configured tolaunch a notebook service instance from the remote computing server orcluster and execute notebook code on the provisioned notebook serviceinteractively.

The agricultural modeling system 100 illustratively comprises ageospatial database 106 configured to store a plurality of data layers107 a-107 e for the agricultural geographic area 101. More specificallyand as perhaps best seen in FIGS. 3-4, the plurality of data layers 107a-107 e for the agricultural geographic area 101 may include a climatedata source, a soil survey data source, a water data source, a satelliteimagery data source, a tree phenotyping data source, and a farm/blockboundary information. Of course, these data source types are exemplary,and any data source for the agricultural geographic area 101 may beused.

Some of the plurality of data layers 107 a-107 e are sourced from thirdparty sources 107 d-107 e (shown with dashed lines), and the remainingdata layers 107 a-107 c are sourced from geospatial and ground-truthdata sources (shown with solid lines). In other words, the remainingdata layers 107 a-107 c have accurate geospatial data embedded therein.

The agricultural modeling system 100 illustratively includes a servercomputing resource 110 in communication with the mobile LiDAR platform102, the plurality of client devices 104 a-104 b, and the geospatialdatabase 106. In the illustrated embodiment, the server computingresource 110 illustratively comprises a processing unit 111 a and astorage device 111 b coupled thereto. In some embodiments, the servercomputing resource 110 comprises resources in a cloud computing platform(i.e. infrastructure as a service (IaaS)), such as Amazon Web Services(AWS), Microsoft Azure, or Google Cloud Platform. Of course, in otherembodiments, the server computing resource 110 may comprise anon-premises computing server, or computing cluster (i.e. an amalgamationof computing resources) or a combination of the on-premises computingserver/cluster and the cloud computing platform.

The server computing resource 110 is configured to geographicallyreference the plurality of data layers 107 a-107 e fused with the 3Dpoint cloud data 103 of the agricultural geographic area 101. As will beappreciated, the data fusion comprises integration/combination of theplurality of data layers 107 a-107 e and the 3D point cloud data 103.

The server computing resource 110 is configured to generate amulti-layered data model 112 for the geographically referenced datalayers 107 a-107 e fused with the 3D point cloud data 103 of theagricultural geographic area 101 for providing interactive analysis andvisualization of agricultural information alongside collaboratorsthrough the client devices 104 a-104 b with LiDAR-based high resolution3D plant model. In the multi-layered data model 112, each of theplurality of data layers 107 a-107 e comprises a separate data source.

As perhaps best seen in diagrams 1100, 1120 of FIGS. 4-5, the servercomputing resource 110 is configured to execute a layer resolvingalgorithm. The layer resolving algorithm may comprise a scaling step foreach the plurality of data layers 107 a-107 e to properly size arespective data layer with the 3D point cloud data 103 of theagricultural geographic area 101.

For each layer, one polygon defines the geographic boundaries. Layer 0is the 3D point cloud data 103, representing the truth value orfoundation polygon (i.e. having the highest accuracy and confidencevalues). As discussed hereinabove, the 3D point cloud data 103 has thehighest resolution. In some embodiments, the 3D point cloud data 103 hasmm-level pixel precision, and each plant is geolocated within the 3Dpoint cloud data (i.e. each detected plant has an associated geolocationvalue). Some of the plurality of data layers 107 a-107 e may comprisemulti-dimensional polygons (e.g. having height data values), forexample, the 3D point cloud data 103.

A given client device 104 a from the plurality of client devices isconfigured to selectively render the multi-layered data model 112. Asillustrated, the given client device 104 a renders the multi-layereddata model 112 with the data sources included in three data layers 107a-107 e while the other client device 104 b renders the samemulti-layered data model 112 with a different data source groupaggregated from four data layers 107 a-107 e. The given client device104 a may comprise a notebook client device, and each data layer 107a-107 e may be associated with at least one notebook workflow.

Some or all of the plurality of data layers 107 a-107 e for theagricultural geographic area 101 have a resolution less than or equal tothe 3D point cloud data 103 of the agricultural geographic area. Inother words, each pixel or point in the point cloud covers a spacesmaller than a respective space for a pixel size in the plurality ofdata layers 107 a-107 e.

The server computing resource 110 and the given client device 104 a areconfigured to execute a programming code script 113 uploaded from thegiven client device. The given client device 104 a is configured toselectively render the multi-layered data model 112 based upon theprogramming code script. In other words, in addition to the selectiverendering of the multi-layered data model 112 (which can be unique foreach client device 104 a-104 b), the respective user of the given clientdevice 104 a may provide custom code to provide unique renderings anddata fusion functionality.

The given client device 104 a is configured to upload an additional datasource 114 to the server computing resource 110. The server computingresource 110 is configured to geographically reference and fuse theadditional data source 114 and the 3D point cloud data 103 of theagricultural geographic area. The server computing resource 110 isconfigured to generate a different multi-layered data model 112 for eachclient device 104 a-104 b using a respective different plurality of datalayers 107 a-107 e.

Although only two client devices are shown for illustrative clarity, theagricultural modeling system 100 is configured to serve a large numberof client devices 104 a-104 b. Moreover, the agricultural modelingsystem 100 may organize the plurality of client devices 104 a-104 b intosubsets, each subset being associated with an institutional client. Theserver computing resource 110 is configured to securely store data foreach of the institutional clients of the agricultural modeling system100. For example, some of the different plurality of data layers 107a-107 e may be proprietary to a given institutional client; therefore,the server computing resource 110 is configured to only allow access tothe different plurality of data layers for client devices 104 a-104 bassociated with the institutional client. On the other hand, some of thedifferent plurality of data layers 107 a-107 e may be publicly availabledatasets, which are available to all the client devices 104 a-104 b,regardless of the associated institutional client. Similarly, anyuploaded programming code script 113 or uploaded additional data source114 are maintained securely for the institutional client associated withthe given client device 104 a, and these are fully isolated from otherunrelated client devices.

Advantageously, the agricultural modeling system 100 may address thescalability problem with geospatial data fusion in agriculturaltechnologies. Firstly, the agricultural modeling system 100 may placethe server computing resource 110 partially or wholly into a computingcluster 210 (FIG. 6A) hosted in a public or private cloud computingplatform (e.g. IaaS), which provides virtually infinite scalability forboth storage and compute needs. Secondly, for institutional clients withproprietary datasets, the agricultural modeling system 100 is capable ofingesting these specialized datasets and overlaying them with publiclyavailable domain datasets for selective rendering to the users of theinstitutional client. Thirdly, the agricultural modeling system 100permits institutional clients to have a subset of client devices 104a-104 b that can readily collaborate and share custom code scripts anddata.

Referring briefly to FIG. 2, a method for operating the server computingresource 110 in an agricultural modeling system 100 for processing datafor an agricultural geographic area 101 is now described with referenceto a flowchart 1000, which begins at Block 1001. The agriculturalmodeling system 100 comprises a mobile LiDAR platform 102 configured togenerate 3D point cloud data 103 of the agricultural geographic area101, a plurality of client devices 104 a-104 b, and a geospatialdatabase 106 configured to store at least one data layer 107 a-107 e forthe agricultural geographic area. The method includes receiving the 3Dpoint cloud data 103 of the agricultural geographic area 101 from themobile LiDAR platform 102, and receiving the at least one data layer 107a-107 e for the agricultural geographic area 101 from the geospatialdatabase 106 (Block 1002). The method further comprises geographicallyreferencing the at least one data layer 107 a-107 e fused with the 3Dpoint cloud data 103 of the agricultural geographic area 101 (Block1003). The method also includes generating a multi-layered data model112 for the geographically referenced at least one data layer 107 a-107e fused with the 3D point cloud data 103 of the agricultural geographicarea 101 for providing interactive analysis and visualization ofagricultural information alongside collaborators with LiDAR-based highresolution 3D plant model. (Block 1004). A given client device 104 afrom the plurality of client devices is configured to selectively renderthe multi-layered data model 112. The method ends at Block 1005.

Referring now additionally to FIG. 6A, another environment where theagricultural modeling system 200 may be implemented and deployed,according to some embodiments of the invention, is now described. Inthis embodiment of the agricultural modeling system 200, those elementsalready discussed above with respect to FIGS. 1-5 are incremented by 100and most require no further discussion herein. This embodiment differsfrom the previous embodiment in that this agricultural modeling system200 illustratively includes an external load balancer 215 deployedbetween the server computing resource 210 (e.g. illustrated computingcluster) and the client device 204 a. A series of development andoperations (DevOps) pipelines 222 are managed by the client device 204 bof an infrastructure engineer and are indirectly connected with thecluster control plane 221 via a cluster application manager 226 and acontainer registry service 227. Meanwhile, the infrastructure engineerat another client device 204 b, a cluster control plane 221, andmonitoring and logging services 225 are governed by an identity andaccess controller 224. In addition, the authentication and authorizationof the client device (e.g. plant scientists' computers) 204 a is alsogoverned by the identity and access controller 224 through a frontendportal 216. The server computing resource 210 illustratively includes afrontend portal 216 serving the internet traffic of the client device204 a via the external load balancer 215 and an ingress controller 218.The frontend portal 216 is also capable of provisioning and managing thenotebook server 228 a-228 b and bridging it with the client device 204a. In notebook services 217, the notebook server 228 a-228 b can receivea request from the client device 204 a selectively executing a notebookimplementation file 219 a with an input geospatial dataset 219 b. Tofurther scale the notebook service performance and capacity, a notebookcompute cluster 220 can be requested and connected to the notebookserver 228 a-228 b. All stateful information and layered datasets aredesigned to store into a backend data storage system 223, which providesa persistent storage layer for both the notebook services 217 and thenotebook compute cluster 220 with various underlying data storage typessuch as block storage 229 a, file storage 229 b, and object storage 229c. In the server computing resource 210, each of service components isdesigned to be platform independent and cloud agnostic, and thus can bematerialized into different code implementations and deployment forms aslong as their service application programming interfaces (APIs) arecompatible.

It should be appreciated that the illustrated embodiment is implementedas cloud agnostic, for example, the illustrated data analytics platformcan be transparently deployed atop of public IaaS such as MicrosoftAzure Cloud, Amazon Web Services, and Google Cloud Platform. Of course,this platform is merely exemplary and other private or public cloudcomputing platforms could be used. For example, and not by way oflimitation, the definition of a notebook file 219 may be implemented bya Jupyter notebook JSON format (i.e. using Jupyter Notebook open sourceframework, as available from the Project Jupyter of NumFOCUS, Inc. ofAustin, Tex.) or Zeppelin notebook JSON format, the notebook server 228a-228 b may be extended to support the messaging protocol of Jupyternotebook server and its variations such as the messaging protocol ofInteract server or the messaging protocol of Zeppelin server, theidentity and access controller 224 may be implemented by the managedAzure Active Directory (AD) service or AWS Identity and AccessManagement (IAM) service, the notebook compute cluster 220 may beimplemented by a Dask parallel computing framework or a Hadoopdistributed computing framework, and the backend data storage system 223may be connected with Azure Data Lake Storage Gen2 or Amazon ElasticBlock Store (EBS), Amazon Elastic File System (EFS), and Amazon SimpleStorage Service (S3).

Referring now additionally to FIGS. 6B-6D, a diagram 1200 showsschematic diagrams of data flows and service components for theagricultural modeling system 200. The agricultural modeling system 200illustratively includes a data analytics platform 236 outputting toprecision agriculture solutions 230. The precision agriculture solutions230, for example, provides the actionable insights derived from theagricultural modeling system 200, such as yield prediction, soil type byplant, relationships by variety, holistic operation view, carboncredits, treatment plan of native plants, and irrigation and watermanagement. The agricultural modeling system 200 illustratively includesedge collection devices 233 with the inherited data sources 234, andthird party data sources 235, all being ingested into the data analyticsplatform 236. The data analytics platform 236 cooperates with a publiccloud platform 231, and/or a private cloud platform 232 for data storageand compute needs.

More specifically, the edge collection devices 233 illustrativelyinclude a grove tracker collection vehicle, a yield tracker collectionvehicle, an aerial multi/hyper spectral drone, a greenhouse phenotypingscanner, and so on. The inherited data sources 234 may include a groundLiDAR dataset, an aerial LiDAR dataset, a GPS trajectory dataset, anaerial imagery dataset, a GeoJSON dataset, and a GeoTIFF dataset. Thethird party sources 235 may include a publicly or privately availablesatellite imagery dataset, a United States soil survey dataset, watertable dataset, a weather dataset, and a climate dataset.

The data analytics platform 236 illustratively comprises a userinterface outputting the precision agriculture solutions 230, and adeveloper interface receiving the materialized solution requirements.The data analytics platform 236 comprises an application catalog module,a data catalog and services module, a machine learning (ML) modelregistry module, and a container image registry module. The applicationcatalog module includes a 3D Point cloud classification, mapping,reprojection, Digital Terrain Models (DTM)/Digital Elevation Models(DEM)/Digital Surface Models (DSM) generation, morphology explorer,georeferencing, orthoimage, and 3D model reconstruction. The datacatalog and service module includes spatio-temporal NoSQL database,relational database management system (RDBMS), a workspace, an objectstore, and an external source integration. The ML model registry moduleincludes deep forest data, and LiDAR trunk extraction. The containerimage registry module comprises a runtime module, a deep learningruntime module, and a Dask/Spark cluster runtime module.

The data analytics platform 236 illustratively comprises a workflowmodule, a cluster framework, a workflow scheduler, and an infrastructureDevOps module. The workflow module, for example, may include a series ofcomputational pipelines to process the workloads of 3D point cloudclassification, mapping, reprojection, DTM/DEM/DSM generation,morphology explorer, georeferencing, orthoimage, and 3D modelreconstruction.

Diagram 1300 illustratively shows an exemplary data flow for a digitaltwins application of plant phenotyping study enabled by the agriculturalmodeling system 200. Diagram 1400 illustratively shows another exemplarydata flow for a vitality tracker pipeline application driven by theagricultural modeling system 200. Of course, these applications aremerely provided as a domain-specific case study and other applicationsare possible, such as a carbon credits application: for evaluation andcalculations of carbon usage and associated credits via calculatedbiomass in combination; native plant monitoring: identification,measurement and logging of native tree health versus nearby grower cropsusing datasets in conjunction with local data (in addition impact ofnative trees on primary grower crop(s)); irrigation and watermanagement: fusion of irrigation system layouts and IoT sensor datasources with processed data within the agricultural modeling system 200;and advanced yield prediction: combination of processed plant data, andactual yield data with ML/AI model to predict future yield within theagricultural modeling system.

Referring now additionally to FIGS. 7A-10, diagrams 1500 & 1600, 1700,1710, 1720 respectively show relationships by variety, yield prediction,soil type-by-plant, and efficiency of irrigation system. Diagram 1730 isa holistic operation view of the data fusion, showing the sources forthe predicted fertility, yield, and irrigation.

Diagrams 1500, 1600 show georeferenced current production inventory on aplant-by-plant basis. The agricultural modeling system 200 may determinehistoric changes in inventory, merge current and historic productiondata, and merge current field observations, soil moisture, and scoutingreports. As shown in diagram 1700, the agricultural modeling system 200may predict yield, and as shown in diagram 1720, assess impact of fieldconditions such as irrigation, variety, fertility on production. Asshown in diagram 1710, the agricultural modeling system 200 may compareperformance of blocks based on variety, soil type, field conditions, andpests.

In the following, an exemplary implementation of the agriculturalmodeling system 100, 200 is now described.

System Architecture

Analytics Environment

The agricultural modeling system 100, 200 includes a cloud-agnosticcontainerized application platform in support of on-demand dataanalytics, ML and AI. The application stack is containerized to supportexecution on multiple IaaS providers and in the public cloud, theprivate cloud, the hybrid cloud, or the edge computing deviceconfiguration. This may comprise the integration of open source notebookruntimes such as Jupyter or Zeppelin notebook frameworks as part of itsdata analytics platform. The agricultural modeling system 100, 200 isconfigured for auto scaling to respond to data size, computerequirements and overall input/output needs “on the fly” and on a peruser basis. This can be configured in a shared use model or an isolatedinstance (per client) model.

The agricultural modeling system 100, 200 is a large-scale geospatialdata analysis, integration, and visualization platform that enables endusers to upload their own or third party data source, analyze italongside collaborators (i.e. plant scientists or team members in thesame organization) with LiDAR-based precision point cloud dataset fusedwith other public data sources (e.g. aerial or satellite imagery, soil,water, and other climate datasets), build historically geo-rectified andfused presentation through the layered data model, and share and publishreproducible analytics workflows and data artifacts.

The system design of agricultural modeling system 100, 200 is conductedby the cloud-agnostic principle with a scalable and immutableinfrastructure, which enables transparently scaling user's applicationsand workloads across multiple IaaS providers with multiple deploymentstructures: the public cloud, the private cloud, the hybrid cloud, orthe edge computing device.

Deployment Pattern

The production deployment of the agricultural modeling system 100, 200supports at least four different infrastructure types: 1) single server:a single computing server hosted on either on-premises or off-premises(edge) computing environment; 2) single cloud: a single computingcluster hosted on either a single public or private cloud environment;3) multi-cloud: a group of computing clusters spun up on theinfrastructures provided by multiple public cloud providers; or 4)hybrid: a combination of on-premises and public cloud computingenvironments. The last two deployment approaches (multi-cloud andhybrid) are used to improve the business and technical challengesinherited from the single cloud environment or region: networking;workload portability and autoscaling; data protection; monitoring andlogging; and developer operations.

User Interface

The agricultural modeling system 100, 200 permits users to executeanalytics scripts, modify exposed parameters and add functionality toscripts to enhance the final output dataset. Also, bundled data,software code and user interfaces can be created and made available foruse by other web enabled systems. The agricultural modeling system 100,200 includes a custom code interface and integration, and an extensibleprogramming interface. This supports the use of standard industry bestpractice analytics programming languages, such as Python, R, Juliaprogramming language, etc. The agricultural modeling system 100, 200also supports the use of custom analytics scripts and re-usable APIs andlibraries.

The agricultural modeling system 100, 200 is capable of supporting theindustry-standard notebook framework with multi-programming runtimesupport, which enables users to seamlessly add their own custom code(e.g. Python, R, Julia) and documentation (e.g. Markdown cells) into theexisting workflows. Depending on the integration granularity, the codeinteraction and data exchange between the agricultural modeling system100, 200 core APIs/workflow and users' custom code can be implementedthrough two different levels: cell-to-cell or notebook-to-notebook. Thecode or Markdown cell is a basic programming unit defined in thenotebook development interface. The cell-level integration provides afine-grained programming interaction between users' custom code andagricultural modeling system 100, 200 and enables users to furthercustomize the standardized data processing workflow based-on their dataprocessing logic. For example, users can leverage the flexibility ofcode cells to add, modify, or remove the selected analysis steps to fittheir requirements. On the other hand, the notebook-level integrationabstracts away the complexity of individual implementation of dataprocessing workflow and allows a rapid assembly between user-defined andstandardized workflows with a plug and play experience.

3rd Party Data Integration

Data Domain

In the agricultural modeling system 100, 200, data sources can includethe post processed LiDAR data collection made available directly frominternal databases and file stores. Datasets, analytics scripts and codecan be bundled and encapsulated ensuring strict data privacy andisolation between clients and customers. The agricultural modelingsystem 100, 200 integrates directly into the internal ground/aerialLiDAR data sources, which provide its foundational layer zerogeo-referenced dataset. Also, for data security, the integration intodata sources is secure and encrypted with the managed encryption keys.This security can be maintained in both a single and multi-tenantconfiguration.

The agricultural modeling system 100, 200 can upload, use and leveragethird party datasets to be fused with layer zero data. The third partydata can be uploaded via a transactional fashion, a batch mechanism, ora direct database integration. The layered data model in theagricultural modeling system 100, 200 is a primary to supportreproducibility, interactive nature, and collaboration across differentdata science teams or organizations. The agricultural modeling system100, 200 is built upon the geo-rectified data model where eachgeospatial layer is automatically versioned and connected to itsprovenance information describing how it was generated. Each geospatiallayer is also associated with the specific notebook-based workflow inwhich it was prepared or updated. The input and output data betweenupstream and downstream analysis steps are traced and versioned by datalineage that is guaranteed by the immutable data storage systemfundamentally. When a data processing workflow is shared or copied, allits data dependencies such as layered input data sources, pipelineconfigurations, and other metadata information are replicated andversioned with it. With the same data dependencies and a workflow copy,users can reproduce an identical intermediate dataset from each dataanalysis step as well as the final output and report aggregated from allsteps inside the whole analytics workflow. The default supporting datatypes include ground/aerial LiDAR data sources, aerial imagery datasources, and other industry standard geospatial formats such as GeoJSON,GeoTIFF, COG, GeoPackage and so on. The available data types can beeasily extended to support a new analytics application or adapt a newdata layer.

Other features relating to agricultural modeling are disclosed inco-pending applications: titled “Systems And Methods For MonitoringAgricultural Products,” application Ser. No. 16/707,198; titled“Forestry Management Tool For Assessing Risk Of Catastrophic TreeFailure Due To Weather Events,” application Ser. No. 16/603,159; andU.S. patents: titled “Systems And Methods For Determining Crop YieldsWith High Resolution Geo-Referenced Sensors,” U.S. Pat. No. 10,534,086;titled “Systems And Methods For Determining Crop Yields With HighResolution Geo-Referenced Sensors,” U.S. Pat. No. 10,539,545; titled“Systems And Methods For Monitoring Agricultural Products,” U.S. Pat.No. 10,371,683; titled “Systems And Methods For Monitoring AgriculturalProducts,” U.S. Pat. No. 9,939,417; titled “Systems And Methods ForMonitoring Agricultural Products,” U.S. Pat. No. 10,520,482; titled“Systems And Methods For Determining Crop Yields With High ResolutionGeo-Referenced Sensors,” U.S. Pat. No. 10,151,839; and titled “SystemsAnd Methods For Determining Crop Yields With Lidar Sensors,” U.S. Pat.No. 9,983,311, all incorporated herein by reference in their entirety.

Many modifications and other embodiments of the present disclosure willcome to the mind of one skilled in the art having the benefit of theteachings presented in the foregoing descriptions and the associateddrawings. Therefore, it is understood that the present disclosure is notto be limited to the specific embodiments disclosed, and thatmodifications and embodiments are intended to be included within thescope of the appended claims.

The invention claimed is:
 1. An agricultural modeling system forprocessing data for an agricultural geographic area, the agriculturalmodeling system comprising: a mobile light detection and ranging (LiDAR)platform configured to generate three-dimensional (3D) point cloud dataof the agricultural geographic area; a plurality of client devices; ageospatial database configured to store at least one data layer for theagricultural geographic area and having a resolution less than or equalto the 3D point cloud data of the agricultural geographic area; and aserver computing resource in communication with said mobile LiDARplatform, said plurality of client devices, and said geospatialdatabase, said server computing resource configured to scale each datalayer with the 3D point cloud data of the agricultural geographic area,geographically reference the at least one data layer fused with the 3Dpoint cloud data of the agricultural geographic area, and generate amulti-layered data model for the geographically referenced at least onedata layer fused with the 3D point cloud data of the agriculturalgeographic area for providing interactive analysis and visualization ofagricultural information alongside collaborators with LiDAR-based highresolution 3D plant model; a given client device from said plurality ofclient devices configured to upload an additional data source to saidserver computing resource, said server computing resource configured togeographically reference and fuse the additional data source and the 3Dpoint cloud data of the agricultural geographic area, and selectivelyrender the multi-layered data model including the additional datasource, the multi-layered data model being unique to the given clientdevice.
 2. The agricultural modeling system of claim 1 wherein said atleast one data layer for the agricultural geographic area comprises aplurality of data layers for the agricultural geographic area comprisinga climate data source, a soil survey data source, a satellite imagerydata source, and a tree phenotyping data source.
 3. The agriculturalmodeling system of claim 1 wherein the 3D point cloud data comprises aKeyhole Markup Language polygon defining the agricultural geographicarea.
 4. The agricultural modeling system of claim 1 wherein said servercomputing resource and said given client device are configured toexecute a programming code script; and wherein said given client deviceis configured to selectively render the multi-layered data model basedupon the programming code script.
 5. The agricultural modeling system ofclaim 4 wherein said server computing resource and said given clientdevice are configured to execute the programming code script based upona cell-to-cell basis.
 6. The agricultural modeling system of claim 1wherein said server computing resource is configured to generate adifferent multi-layered data model for each client device using arespective different at least one data layer.
 7. The agriculturalmodeling system of claim 1 wherein said given client device comprises anotebook client device; and wherein said at least one data layer isassociated with at least one notebook workflow.
 8. The agriculturalmodeling system of claim 1 wherein said mobile LiDAR platform comprisesat least one of an airborne platform and a ground platform.
 9. A servercomputing resource in an agricultural modeling system for processingdata for an agricultural geographic area, the agricultural modelingsystem comprising a mobile light detection and ranging (LiDAR) platformconfigured to generate three-dimensional (3D) point cloud data of theagricultural geographic area, a plurality of client devices, and ageospatial database configured to store at least one data layer for theagricultural geographic area and having a resolution less than or equalto the 3D point cloud data of the agricultural geographic area, theserver computing resource comprising: a processing unit and storagedevice cooperating therewith and configured to receive the 3D pointcloud data of the agricultural geographic area from the mobile LiDARplatform, and receive the at least one data layer for the agriculturalgeographic area from the geospatial database, scale each data layer withthe 3D point cloud data of the agricultural geographic area,geographically reference the at least one data layer fused with the 3Dpoint cloud data of the agricultural geographic area, and generate amulti-layered data model for the geographically referenced at least onedata layer fused with the 3D point cloud data of the agriculturalgeographic area for providing interactive analysis and visualization ofagricultural information alongside collaborators with LiDAR-based highresolution 3D plant model; a given client device from the plurality ofclient devices configured to upload an additional data source to saidprocessing unit, said processing unit configured to geographicallyreference and fuse the additional data source and the 3D point clouddata of the agricultural geographic area, and selectively render themulti-layered data model including the additional data source, themulti-layered data model being unique to the given client device. 10.The server computing resource of claim 9 wherein said at least one datalayer for the agricultural geographic area comprises a plurality of datalayers for the agricultural geographic area comprising a climate datasource, a soil survey data source, a satellite imagery data source, anda tree phenotyping data source.
 11. The server computing resource ofclaim 9 wherein the 3D point cloud data comprises a Keyhole MarkupLanguage polygon defining the agricultural geographic area.
 12. Theserver computing resource of claim 9 wherein said processing unit isconfigured to execute a programming code script; and wherein the givenclient device is configured to selectively render the multi-layered datamodel based upon the programming code script.
 13. The server computingresource of claim 12 wherein said processing unit is configured toexecute the programming code script based upon a cell-to-cell basis. 14.The server computing resource of claim 9 wherein said processing unit isconfigured to generate a different multi-layered data model for eachclient device using a respective different at least one data layer. 15.A method for operating a server computing resource in an agriculturalmodeling system for processing data for an agricultural geographic area,the agricultural modeling system comprising a mobile light detection andranging (LiDAR) platform configured to generate three-dimensional (3D)point cloud data of the agricultural geographic area, a plurality ofclient devices, and a geospatial database configured to store at leastone data layer for the agricultural geographic area and having aresolution less than or equal to the 3D point cloud data of theagricultural geographic area, the method comprising: receiving the 3Dpoint cloud data of the agricultural geographic area from the mobileLiDAR platform, and receiving the at least one data layer for theagricultural geographic area from the geospatial database; scaling eachdata layer with the 3D point cloud data of the agricultural geographicarea; geographically referencing the at least one data layer fused withthe 3D point cloud data of the agricultural geographic area; andgenerating a multi-layered data model for the geographically referencedat least one data layer fused with the 3D point cloud data of theagricultural geographic area for providing interactive analysis andvisualization of agricultural information alongside collaborators withLiDAR-based high resolution 3D plant model; a given client device fromthe plurality of client devices configured to upload an additional datasource to the server computing resource, the server computing resourceconfigured to geographically reference and fuse the additional datasource and the 3D point cloud data of the agricultural geographic area,and selectively render the multi-layered data model including theadditional data source, the multi-layered data model being unique to thegiven client device.
 16. The method of claim 15 wherein the at least onedata layer for the agricultural geographic area comprises a plurality ofdata layers for the agricultural geographic area comprising a climatedata source, a soil survey data source, a satellite imagery data source,and a tree phenotyping data source.
 17. The method of claim 15 whereinthe 3D point cloud data comprises a Keyhole Markup Language polygondefining the agricultural geographic area.
 18. The method of claim 15further comprising executing a programming code script; and wherein thegiven client device is configured to selectively render themulti-layered data model based upon the programming code script.
 19. Themethod of claim 18 further comprising executing the programming codescript based upon a cell-to-cell basis.
 20. The method of claim 15further comprising generating a different multi-layered data model foreach client device using a respective different at least one data layer.